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A Task Allocation Strategy for Collaborative Learning in Virtual Reality

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第5期41卷 3080-3103页

作者： Lin, Yi Huang, Xiaolong Guo, Peng Chen, Xingwei Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350018 Peoples R China

Collaborative learning is widely applied in practical education due to its high efficiency and positive effectiveness. Virtual reality (VR) has driven the development of collaborative learning with its immersive and interactive characteristics. Task allocation, as the first step in initiating collaboration when applying VR to collaborative learning, plays a crucial role. However, it has not received enough attention and lacks in-depth research. Therefore, we first analyzed the characteristics of VR collaborative learning and found that considering the influence of five key factors on tasks allocation can improve learning outcomes. Consequently, we constructed a model applicable to VR collaborative learning. Subsequently, we proposed a two-way task allocation strategy that balances the interaction between learners' intentions and the requirements of tasks. Then, by invoking an improved sparrow search algorithm for optimization calculations, the assignment results were automatically computed after going through three stages. Finally, evaluation experiments were conducted to validate the feasibility and correctness of our model and strategy by comparing them with other methods. The results indicate that compared to conventional collaborative learning, VR collaborative learning yields better learning outcomes. Moreover, our strategy demonstrates higher learning efficiency and better learning effects compared to using self-negotiation and Agent-based strategies for assigning VR collaborative tasks, providing a valuable reference for the continuous exploration of this scientific issue.

关键词： Virtual reality collaborative learning task allocation sparrow search algorithm

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Cutting-edge approaches to specific energy prediction in TBM disc cutters: Integrating COSSA-RF model with three interpretative techniques

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UNDERGROUND SPACE 2025年 22卷 241-262页

作者： Zhou, Jian Liu, Zijian Li, Chuanqi Du, Kun Yang, Haiqing Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Grenoble Alpes Univ CNRS UMR 5521 Lab 3SR F-38000 Grenoble France Chongqing Univ Sch Civil Engn State Key Lab Coal Mine Disaster Dynam & Control Chongqing 400045 Peoples R China

Specific energy (SE) is an important index to measure crushing efficiency in mechanized tunnel excavation. Accurate prediction of the SE of tunnel boring machine disc cutters is important for optimizing the crushing process, reducing energy consumption, and minimizing machine wear. Therefore, in this paper, the sparrow search algorithm (SSA), combined with six chaotic mapping strategies, is utilized to optimize the random forest (RF) model for predicting SE, referred to as the COSSA-RF prediction models. For this purpose, an SE prediction database was established for training and validating model performance, encompassing 160 sets of experimental data, each with six input parameters: uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), disc cutter diameter (D), cutter tip width (T), cutter spacing (S), and cutter penetration depth (P), along with a target parameter, SE. The evaluation results indicate that the COSSA-RF models demonstrate superior performance compared to other four machine learning models. In particular, the Chebyshev map-SSA-RF (CHSSA-RF) model achieves the most satisfactory prediction accuracy among all models, resulting in the highest coefficient of determination R2 and dynamic variance-weighted global performance indicator values (0.9756 and 0.0814) and the lowest values of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) (6.4742, 4.0003, and 20.41%). Lastly, the results of interpretability analysis of the best model through SHapley Additive exPlanations, local interpretable model-agnostic explanations, and Vivid methods show that the importance of input parameters ranked as follows: UCS, BTS, P, S, T, and D. Moreover, interactions between parameters (UCS and BTS, BTS and P, and BTS and S) significantly influence the model predictions.

关键词： Specific energy Chaotic mapping Random forest sparrow search algorithm Model interpretation

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XGBoost-Based Power Grid Fault Prediction with Feature Enhancement: Application to Meteorology

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Computers, Materials & Continua 2025年第2期82卷 2893-2908页

作者： Kai Liu Meizhao Liu Ming Tang Chen Zhang Junwu Zhu State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210024China State Grid Jiangsu Electric Power Co. Ltd.Information&Yangzhou Power Supply BranchYangzhou225000China School of Information Engineering Yangzhou UniversityYangzhou225127China

The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to enhance the optimization accuracy of the sparrow search algorithm. Finally, we construct an XGBoost-based model for the classification prediction of power grid meteorological faults and optimize the hyperparameters such as the optimal tree depth, optimal learning rate, and optimal number of iterations using an enhanced sparrow search algorithm. Experimental results demonstrate that our method outperforms the baseline models in predicting power grid faults accurately.

关键词： Meteorological factors gradient boosting decision tree sparrow search algorithm XGBoost

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Region-aware dynamic job scheduling and resource efficiency for load balancing based on adaptive chaotic sparrow search optimization and coalitional game in cloud computing environments

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2024年 221卷

作者： Khaleel, Mustafa Ibrahim Univ Sulaimani Kurdistan Reg Govt Coll Sci Comp Dept Sulaimani 46001 Iraq

Uneven distribution of workloads represents a significant challenge in cloud data centers, negatively impacting the efficient utilization of resources. To address this problem and optimize the performance-to-profit (PP) ratio, we introduced the Regional Awareness dynamic Scheduling algorithm (RASA) through a three-phase approach. In the initial phase, tasks are categorized based on critical parameters like CPU usage, memory utilization, and I/O operations, using a task classification model. The second phase involves estimating the CPU, memory, and I/O capacities of each node. Subsequently, a unique merge-and-split-based coalitional game-theoretic process is employed to group nodes into clusters. In the final phase, we have improved task placement by introducing an enhanced sparrow search algorithm (ISSA) to overcome the slow convergence and local optimization issues found in the traditional SSA. We have incorporated the Tent chaotic mapping approach to enhance both global and local search efficiency during this stage. Additionally, we have fine-tuned fair load distribution using the Cauchy mutation process. We performed simulations using the CloudSim toolkit, and the results underscore the RASA algorithm's superiority in terms of how quickly it converges its precision in task placement and its ability to distribute workloads fairly. Our proposed algorithm achieves a 9% reduction in latency overhead, a 14% decrease in processing time, a 15% reduction in workload imbalance, a 19% decrease in energy consumption, and a 26% decrease in idle periods. Furthermore, it enhances resource availability and efficiency by 22% and 27% respectively, while simultaneously boosting service throughput by 32%.

关键词： sparrow search algorithm Game-based theory Load distribution Cauchy mutation process Chaotic mapping approach

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Optimizing mechanical properties of sustainable industrial fiber composites with integrated neural networks and bio-inspired algorithms

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INDUSTRIAL CROPS AND PRODUCTS 2025年 229卷

作者： Wu, Yan Wang, Bin Chen, Jian Huang, Xinkang Xu, Jun Wei, Wenguang Chen, Kefu South China Univ Technol Plant Fiber Mat Sci Res Ctr Sch Light Ind & Engn State Key Lab Pulp & Paper Engn Guangzhou 510640 Peoples R China Guangzhou Univ Town Guangdong Prov Key Lab Plant Resources Biorefinery 100 West Outer Ring Rd Guangzhou 510006 Peoples R China Kaihua Paper Tradit Technol Res Ctr Kaihua 324300 Peoples R China Shandong Huatai Paper Co Ltd Dongying 257335 Peoples R China

Sustainable natural fiber composites hold promising prospects in various industrial applications due to their ecofriendly attributes and renewable nature. In this study, we introduce an innovative dual-integrated artificial neural network framework enhanced by the sparrow search algorithm (SSA) and genetic algorithm (GA) to overcome the challenges posed by diverse raw materials and complex manufacturing processes. The SSA component improves global exploration and local optimization, resulting in significantly lower mean square error (MSE) and root mean square error (RMSE) compared to traditional backpropagation (BP) neural networks. Further fine-tuning of the initial weights and thresholds via GA substantially boosts model performance. Our experimental analysis demonstrates an impressive optimization rate of 99.88 %, with critical mechanical indices-such as a tearing index of 14.75 mN center dot m2/g, a tensile index of 68.30 N center dot m/g, an elasticity modulus of 4.02 GPa, and air permeability of 6.63 mu m/(Pa center dot s)-being achieved. SHAP analysis further decodes featureproperty relationships, enabling actionable insights for fiber-to-matrix ratio tuning. This comprehensive framework not only enhances prediction accuracy and model interpretability but also offers a scalable solution for the design and optimization of sustainable industrial fiber composites, advancing their application in green bio-composites.

关键词： Sustainable industrial fiber composites Mechanical property optimization Artificial neural networks Heuristic optimization algorithms sparrow search algorithm

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An Evaluation of Dimensionality Reduction and Classification Techniques for Cardiac Disease Diagnosis from ECG Signals with Various Deep Learning Classifiers

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年第1期44卷 416-446页

作者： Karthikeyani, S. Sasipriya, S. Ramkumar, M. Sri Krishna Coll Engn & Technol Dept Elect & Commun Engn Coimbatore 641008 India

Classification of cardiac diseases from electrocardiogram signals is essential for enhancing patient results to minimize healthcare costs, early detection and accurate diagnosis. This research investigates the integration of dimensionality reduction methods with various deep learning classifiers to improve the accuracy and efficient classification of cardiac disease. Uniform Manifold Approximation and Projection combined with Principal Component Analysis is used for dimensionality reduction, that captures both global and local data structures. Deep learning classifiers with convolutional neural networks, capsule networks, recurrent neural networks, graph neural networks, deep long short-term memory and automatical attention-based convolutional neural networks are employed for classification. The Adaptive spiral Flying sparrow search algorithm optimizes classifier parameters for enhance accuracy. Performance is evaluated through various metrics, with area under the receiver operating characteristic curve, accuracy, F1-Score, precision and recall. The proposed method's outcomes are compared with and without optimization to demonstrate the efficiency and attains 92.16%, 96.15%, 91.95%, 94.65%, 91.45% and 92.85% accuracy respectively for each classification method.

关键词： Cardiac disease Electrocardiogram Dimensionality reduction sparrow search algorithm Deep learning classifiers

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Power Quality Disturbance Classification Strategy Based on Fast S-Transform and an Improved CNN-LSTM Hybrid Model

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PROCESSES 2025年第3期13卷 743-743页

作者： Bai, Hao Yao, Ruotian Zhang, Wenhan Zhong, Zhenxin Zou, Hongbo China Southern Power Grid Elect Power Res Inst Guangzhou 510000 Peoples R China Huizhou Power Supply Bur Guangdong Power Grid Huizhou 516000 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

With the increasing complexity of power systems and the widespread application of power electronic equipment, power quality issues have become increasingly prominent, among which power quality disturbances are one of the key factors affecting the stable operation of power systems and the normal functioning of electrical equipment. Current research methods are still limited by feature extraction, insufficient model generalization ability, and strong data dependence. This paper proposes a power quality disturbance classification strategy based on the fast S-transform (FST) and an improved convolutional neural network-long short-term memory (CNN-LSTM) model to achieve accurate classification and identification of various power quality disturbances. Firstly, the FST is employed to process the power quality disturbance signals, enabling efficient analysis and feature extraction while effectively preserving the time-frequency characteristics of the signals and significantly reducing the computational burden. Secondly, to address the limitations of traditional CNN models in power quality disturbance classification, this paper introduces an improved CNN-LSTM hybrid classification model that integrates mechanism fusion. This model improves the classification performance and generalization ability for power quality disturbances by incorporating an enhanced sparrow search algorithm and learning mechanisms. Finally, the proposed strategy is experimentally validated using a large dataset of power quality disturbances. After analysis and comparison, the method proposed in this paper maintains an identification accuracy of over 97% even in strong noise environments when subjected to a single type of disturbance. Under complex conditions involving mixed disturbances of multiple types, the identification accuracy remains above 95%. Compared to existing methods, the proposed method achieves an improvement in identification accuracy by up to 3.2%. Additionally, its identification accu

关键词： power quality disturbance classification fast S-transform (FST) improved convolutional neural network-long short-term memory (CNN-LSTM) model sparrow search algorithm

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A Cellular Network Resource Allocation Strategy Based on D2D Communication

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第7期38卷

作者： Liu, Yi Jiang, Xueqing Li, Guoyan Hong, Jiao Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin Peoples R China

To address the problems of system capacity reduction due to high system interference in highway vehicular communication scenarios, a resource allocation strategy for cellular vehicular communication based on device-to-device (D2D) communication is proposed. A mathematical model is constructed with CUE user channel capacity as the optimization objective, considering resource sharing between device-to-device users (DUE) and cellular users (CUE), and the model is solved through three stages. Firstly, it is proposed to calculate the user channel gain to construct the DUE user channel gain matrix and compare the total channel gain after user multiplexing to manage the user clustering to reduce the interference between users. Secondly, the elite reverse learning strategy and L & eacute;vy flight strategy were introduced to improve the sparrow search algorithm, which increased the convergence speed and the ability to escape from the local optimal solution and optimized the channel matching problem. Finally, the water injection algorithm is invoked to solve the power allocation problem to maximize the channel capacity of CUE. Simulation experimental results show that the strategy achieves a system capacity of 340 bps/Hz while ensuring that the system communication is basically stable.

关键词： highway Internet of Vehicles resource allocation sparrow search algorithm user cluster

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Study of Wind Power Prediction in ELM Based on Improved SSA

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第6期20卷 853-861页

作者： Shao, Lei Huang, Wenxuan Liu, Hongli Li, Ji Tianjin Key Lab New Energy Power Convers Transmiss Tianjin 300384 Peoples R China

This paper proposes a short-term wind power prediction model based on the improved sparrow search algorithm (SSA) and Extreme Learning Machine(ELM) for anomalous wind power information from wind farms. The objective is to enhance the accuracy of short-term wind power prediction. The model employs the extraction of features utilizing raw wind power history data from wind farms, in conjunction with the application of Variable Importance in Projection indices in Partial Least Squares (PLS-VIP). As the ELM network model is susceptible to the influence of randomly generated input weights and thresholds at the outset of training, a solution is proposed whereby the input weights and thresholds of the ELM are optimized using SSA. The optimal weights and thresholds identified by SSA are then applied to the ELM model, thus forming the SSA-ELM model. To address the limitations of traditional SSA, namely its susceptibility to local optimal solutions and poor global search ability, an improved SSA-ELM algorithm is proposed. The improved SSA-ELM algorithm introduces chaotic sequences and an exchange learning strategy to the original SSA. The rationale behind incorporating chaotic sequences is to enhance the quality of the initial solution, ensuring a more uniform distribution of sparrow positions and, consequently, a more diverse sparrow population. This, in turn, enables the algorithm to achieve a more effective global search capability through the utilization of the exchange learning strategy. Subsequently, all the data are fed into the SSA-ELM model for prediction purposes. The simulation results demonstrate that the model exhibits enhanced prediction accuracy and improved practical applicability in wind power prediction. (c) 2025 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： wind power prediction sparrow search algorithm extreme learning machine variable importance in projection indices in partial least squares

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Short-term traffic flow prediction via weight optimization of composite models

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION REsearch 2025年第3期17卷 395-405页

作者： He, Xinyi Hu, Yao Chen, Wangyong Qin, Yutao Wu, Chuliang Lu, Wanlian Guizhou Univ Sch Math & Stat Guiyang Peoples R China Guizhou Univ Sch Math & Stat South Sect Huaxi Rd Guiyang 550025 Peoples R China

Accurate prediction of peak-hour traffic flow is crucial for congestion management. Neural network models have varying performance in different domains, limiting their effectiveness in traffic data. To address this, we propose the SSA-BiLSTM-BP-Elman ensemble model. It integrates bi-directional long short-term memory (BiLSTM), back propagation (BP) neural network, and Elman network. The model uses the sparrow search algorithm (SSA) to optimize the weight distribution to improve the prediction accuracy. Initially, the BiLSTM, BP, and Elman models are parameter-optimized by the Bayesian approach. SSA then assigns unique weights to each model based on the characteristics of the extracted data features. Results from an application to UK high-speed traffic flow data show that SSA significantly improves the accuracy of model predictions. This integrated model effectively utilizes the strengths of each model by assigning appropriate weighting coefficients by SSA, thus improving the overall prediction performance.

关键词： Short-term traffic flow prediction composite model sparrow search algorithm bi-directional long short-term memory back propagation neural network Elman network

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